Analysis, Design and Control of Biological Circuits and Bio-Inspired Networks

The regulatory architectures controlling information flow through cellular networks are critical to a systems-level understanding of cellular behaviour and to the development of engineering methods for designing synthetic biological circuits. These regulatory architectures are comprised of the diverse regulatory mechanisms that act at different time scales and act simultaneously to create complex control systems regulating the kinetics of component interactions. By developing techniques for the analysis and design of complex regulatory networks in cells, we aim to better understand how cells regulate their function, to enable the engineering of biological circuits for diagnostic and biosynthetic applications, and to provide new techniques in network science that allow understanding and construction of complex networks that provide robust behaviour in the presence of high levels of uncertainty.The long-term goal of the PI and Prof Murray is to develop a theory of architecture that can be used to understand and design complex networked control systems. We believe that the theory and tools that we develop will be relevant not only to the biology of the cell, but also to engineering systems at a variety of scales and levels of complexity. The common features of all of these classes of systems are the ubiquitous use of interconnection and feedback, the robust yet fragile (RYF) nature of their operation, and the lack of current tools to understand all but selected parts of their operation. To pursue this long-term goal, we have focused on the theory, tools and devices required to systematically analyse various forms of regulation of biological function.Using a combination of modelling and experiments, Prof Murray has explored the role of multiple, simultaneous feed-back mechanisms for providing high performance operation in cells, with robustness to parametric variations. In the past year, he has focused on experimental and model-based characterization of ultra-sensitivity of the galactose circuitry in yeast, which provides an excellent example of a multi-mechanism regulatory strategy. The galactose genetic switch is an example of a system with multiple feedback loops that interact to create robust biological function. Galactose is imported into the cell to activate transcription of the galactose regulon through a four-stage signalling process. Additional theoretical work has helped explore the use of time delays as a design element for shaping the behaviour of biomolecular systems.

The proposal will directly encompass a wide range of benefits including scientific knowledge discovery, skill enhancement and training of people. In addition, it has great potential for societal and economic benefits over a wide variety of timescales. One of the main goals of this proposal is to understand why biochemical systems are composed of multiple feedback loops. Such understanding will lead to new methodologies to design technological systems and to synthesise biochemical systems. Other objectives include the learning of theoretical and experimental approaches to systems and synthetic biology. Such learning will be brought back to Cambridge University and strengthen the UK position in the field of systems and synthetic biology. This may lead to longer-term benefits to medicine where the target is for a personalised medicine, which will be possible when we can have good understating and control of molecular, cell and organ level networks. Accordingly, the potential for commercial application of our work exists wherever a business enterprise can benefit from synthesising biochemical networks. We expect that molecular biologists and in particular pharmaceutical companies will be the obvious and most direct beneficiaries of the results in this proposal. Here, we detail our vision and strategies for promoting the exposure, understanding, and application of the proposed work in both commercial and academic settings. The most prominent business with great potential for an economic impact is the pharmaceutical industry. With large sums of money being spent in drug development and clinical trials every year, it is important to have guarantees that drugs do what they were designed to do. Ideally, doctors would be able to diagnose and treat people based on individual differences, a concept commonly referred to as personalized medicine. At its core, personalized medicine is about combining genetic information with clinical data to optimally tailor drugs and doses to meet the unique needs of an individual patient. Eventually, personalized medicine will be further informed by detailed understanding of the body's distinct repertoire of proteins (proteomics) and complete catalogue of biochemical reactions (metabolomics). Due to the facts that the functional mechanisms of cell biology and physiological systems are still mostly unknown, and that there is a large diversity between these systems, drug development today typically relies on heuristics and trial and error guessing of what systems actually are and how they function. The main problem of such heuristic methods is that mistakes in this industry can be very costly and have devastating outcomes. To help disseminate the results in the proposal to pharmaceutical companies, the PI will take advantage of the Corporate Partnership Programme at Pembroke College (http://www.pem.cam.ac.uk/about/cpp/), where he is a fellow. This unique programme helps partner companies negotiate and gain access to the expertise of leading academics via a dynamic network centred on Pembroke College. Through seminars, workshops and other projects practitioners, researchers and academics can build relationships of benefit to all. The programme has grown from a cluster of contacts mainly in the pharmaceutical sector to a large number of diverse companies. Current partners operate in sectors as different as property, engineering, back-office outsourcing and ICT as well as pharmaceutics. Currently, several major pharmaceutical companies belong to the Corporate Partnership Programme, including Boehringer Ingelheim, F Hoffmann-La Roche Ltd and AstraZeneca. Initial contacts with some of these companies have shown great interest in our research. We plan to take these interactions to the next level as we obtain the first results from this project.